Use rules when the process is predictable
If the inputs, decisions, and next steps can be defined clearly, conventional automation may be simpler to test and operate. Adding a language model should solve a specific problem, such as interpreting varied documents or synthesizing unstructured information.
Use predictive models for repeatable prediction
Forecasting demand, estimating churn risk, and detecting unusual behavior can call for statistical or machine learning methods. Compare them against simple baselines and consider whether the available history supports a useful prediction.
Use agents for bounded, variable workflows
An agent can be useful when a workflow requires interpreting a request and selecting among approved tools or steps. Define what it is allowed to access and change, when it needs review, and what happens if it cannot complete a task.
Improve visibility when the decision is the bottleneck
Sometimes the most valuable solution is a reliable dataset and a dashboard with the right metrics. If leaders cannot see what is happening, a clear reporting system may create more immediate value than another layer of automation. The best solution can combine these approaches.